The basic helix-loop-helix transcription factor family in the sacred lotus, Nelumbo nucifera In summary, this is the first in silico analysis of the GRAS gene family in sacred lotus, which will provide valuable information for further molecular and biological analyses of this important gene family. Eight of the ten PAT1-clade GRAS genes, particularly NnuGRAS-05, NnuGRAS-10 and NnuGRAS-25, were preferentially expressed in rhizome and root. Based on publically available RNA-seq data generated from leaf, petiole, rhizome and root, we found that most of the sacred lotus GRAS genes exhibited a tissue-specific expression pattern. Comparative analysis identified 42 orthologous and 9 co-orthologous gene pairs between sacred lotus and Arabidopsis, and 35 orthologous and 22 co-orthologous gene pairs between sacred lotus and rice. In addition, the gene structures and motifs of the sacred lotus GRAS proteins were characterized in detail. In this study, we identified 38 GRAS genes in sacred lotus ( Nelumbo nucifera), analyzed their physical and chemical characteristics and performed phylogenetic analysis using the GRAS genes from eight representative plant species to show the evolution of GRAS genes in Planta. The GRAS gene family has been well characterized in many higher plants such as Arabidopsis, rice, Chinese cabbage, tomato and tobacco. The GRAS gene family is one of the most important plant-specific gene families, which encodes transcriptional regulators and plays an essential role in plant development and physiological processes. We then calculate that back to how many troops are required to get as close to that required carry capacity, and show this as an output.Genome-wide identification and characterization of GRAS transcription factors in sacred lotus ( Nelumbo nucifera) At that point “a” is distributed so that it gives us the ratio of carry capacity required to get the maximum amount of resources per hour. We keep doing this, until our total revenue does not increase any more. Next up, we do the same, but then with a and a. Our second attempt would be, and our third attempt would be. Given that all scavenges are enabled, we'd start with. Of those three options, we check which gives the highest revenue and keep that one. With this initial “a” we calculate how much we’d get per hour, and save this in a variable. Once we find that one, we can simply multiply the total capacity by the individual elements of “a” in order to find how much capacity we should send on a given scavenge hunt. Now this is a formula that we could start brute-forcing to find the ideal “a”. Let’s assume “a” to be an array representing the spread of carry capacity over the four scavenges, where the sum of the 4 elements <= 1. This function can be summed four times, with a spread of the iCap (or capacity) for all different ratios. This gives the following formula for resources per hour: iCap * iRatio / ((Math.pow(Math.pow(iCap, 2) * 100 * Math.pow(iRatio, 2), 0.45) + 1800) * df) World information for duration_factor used to figure out the formula. By throwing the data of the duration_factor and game_speed into google sheets and letting sheets calculate a power trendline, I found that the formula is game_speed^(-0.55). This seems to be loosely based on the world speed. In this formula, df stands for the duration factor. The following equation is found for the duration of a scavenge: ((Math.pow(Math.pow(iCap, 2) * 100 * Math.pow(iRatio, 2), 0.45) + 1800) * df) Where iCap is the total capacity of all units, and iRatio is 10% for the first hunt, 25% for the second, 50% for the third and 75% for the fourth. In the game javascript the following formula can be found for how much a scavenge brings in: iCap * iRatio – The name of the world (start of the URL). In order for me to add a world, I need the following information: This calculator was approved on under ticket number T13900636.
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